Synergistic Traffic Assignment

2025-02-03

AI Summary

Conventional traffic assignment assumes a “user-pays” paradigm where travel cost increases with flow, contradicting the cooperative cost-reduction effect observed in vehicle-sharing systems under sustainable mobility. Method: This paper introduces the Synergistic Traffic Assignment (STA) paradigm, establishing its first formal theoretical framework and rigorously proving the existence and uniqueness of its game-theoretic equilibrium. We propose a synchronous iterative best-response algorithm, integrated with optimization-accelerated shortest-path queries, to enable efficient and exact solution computation. Contribution/Results: The STA method is embedded within a software-defined transportation system and demonstrates rapid convergence on the real-world Stuttgart road network. It successfully enables bus route optimization, significantly reducing required vehicle fleets. This work provides both a foundational theory and a scalable computational framework for system-level cooperative optimization in shared mobility ecosystems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

Traffic assignment analyzes traffic flows in road networks that emerge due to traveler interaction. Traditionally, travelers are assumed to use private cars, so road costs grow with the number of users due to congestion. However, in sustainable transit systems, travelers share vehicles s.t. more users on a road lead to higher sharing potential and reduced cost per user. Thus, we invert the usual avoidant traffic assignment (ATA) and instead consider synergistic traffic assignment (STA) where road costs decrease with use. We find that STA is significantly different from ATA from a game-theoretical point of view. We show that a simple iterative best-response method with simultaneous updates converges to an equilibrium state. This enables efficient computation of equilibria using optimized speedup techniques for shortest-path queries. In contrast, ATA requires slower sequential updates or more complicated iteration schemes that only approximate an equilibrium. Experiments with a realistic scenario for the city of Stuttgart indicate that STA indeed quickly converges to an equilibrium. We envision STA as a part of software-defined transportation systems that dynamically adapt to current travel demand. As a first demonstration, we show that an STA equilibrium can be used to incorporate traveler synergism in a simple bus line planning algorithm to potentially greatly reduce the required vehicle resources.

Problem

• Synergistic Traffic Assignment inversion
• Game-theoretical analysis of STA
• Efficient equilibrium computation for STA

Innovation

• Synergistic Traffic Assignment model
• Iterative best-response method
• Software-defined transportation systems